Cross validation settings for Classification Discriminant models

I foumd the 'KFold' in classification models can not be controlled when 'HyperparameterOptimizationOptions' is enabled. Here is a snippet of my codes:

discrMdl = fitcdiscr(X,y,'discrimType','linear',...
    'OptimizeHyperparameters', VariableDescriptions,'HyperparameterOptimizationOptions',...
    struct('ShowPlots',0,'Verbose',0,'Kfold',5));

After training, typing

discrMdl.crossval.KFold

gives a number of 10, implying 10-fold crosss validations had been perforemd, although 5-fold was defined in the function. I also tried other numbers, but it is alwasy 10-fold. It does not make sense according to the documentation athttps://www.mathworks.com/help/stats/fitcdiscr.html#bt6d86x-1_sep_shared-HyperparameterOptimizationOptions, unless the hyparameter optimization is only done in a 10-fold cross-validation fashion?

If the 'KFold' is put outside the strucured options, an error message will occur:

When optimizing parameters, validation arguments may only appear in the
'HyperparameterOptimizationOptions' argument.

Also, the resulting model is a ClassificationDiscriminant model rather than ClassificationPartitionedModel. Should the model be the latter given the cross validation was enabled in the hyperparameter optimization, according to the documentation at https://www.mathworks.com/help/stats/fitcdiscr.html#bt6d86x-1-Mdl.

Did I interpret something wrong?

Answer 1

[Aditya Patil]

When you use the crossval function, it creates a partioned model with KFold set to default value of 10. Currently, it's not possible to read the value of Kfold for ClassificationDiscriminant class. I have brought this issue to the concerned people and it might be considered in any future release.